MACHINE LEARNING (2/1 SCU)

Learning Outcomes:

On successful completion of this course, students will be able to: LO1 – Explain fundamental concepts of machine learning; LO2 – Demonstrate feature engineering techniques to get important features with Python; LO3 – Examine the best machine learning model for a given problem using Python; LO4 – Construct supervised and unsupervised learning models for a given problem with python.

Topics:

1. Support Vector Machine;
2. Support Vector Machines;
3. K-nearest neighbors and Naïve-Bayes Classifiers (T);
4. Ensemble Learning;
5. Ensemble Learning (LEC);
6. Decision Trees;
7. K-nearest neighbors and Naïve-Bayes Classifiers;
8. Classification & Regression;
9. Logistic Regression;
10. Data Preparation;
11. Clustering;
12. Project Presentation;
13. Decision Trees;
14. Machine Learning Landscape;
15. End-to-End Machine Learning Project;
16. Clustering LAB.